1. Field of the Invention
This invention relates to the process of fabricating semiconductor chips. More specifically, the invention relates to a method and apparatus for computing dummy feature density for performing dummy filling that improves the quality of a Chemical-Mechanical Polishing (CMP) process.
2. Related Art
The miniaturization of integrated circuits has been a key driving force behind technological innovations. Miniaturization of transistors and interconnects has occurred because of the rapid advances in various fabrication technologies, such as “Chemical Mechanical Polishing” (CMP). CMP is used to reduce topography variation of a wafer, and today, it has become a critical process in the semiconductor fabrication flow. CMP polishes a wafer surface to create a flat (planarized) surface. Specifically, CMP combines the chemical removal effect of an acidic or basic fluid solution with the “mechanical” effect provided by polishing with an abrasive material.
Today, CMP is used extensively in the semiconductor fabrication flow. Although CMP makes the wafer surfaces smoother, it does not completely eliminate topography variations. If these post-CMP topography variations are too large, they can cause serious problems during subsequent fabrication steps. For example, these variations can cause defocusing during photolithography, which can result in low fabrication yields and large performance variations.
Moreover, integrated circuits usually contain multiple metal interconnect layers. Hence, the post-CMP topography variations can accumulate over several layers and exacerbate these problems. Furthermore, since miniaturization is expected to continue at a rapid pace (as predicted by Moore's Law), reducing post-CMP topography variation is expected to become even more crucial in the future. Hence, it is critically important to reduce post-CMP topography variation.
Recent studies have shown that post-CMP topography variation depends strongly on feature density. Specifically, it has been shown that the post-CMP topography variation can be reduced by decreasing the non-uniformity of feature density. For example, during the aluminum interconnect fabrication process, the aluminum metal layer is first deposited on the wafer and patterned. Next, an oxide layer that serves as the inter-level dielectric is deposited. Since the underlying metal layer topography is usually not smooth after being patterned, the oxide layer topography is also not smooth. Hence, an oxide CMP process is applied to smooth the wafer surface topography so that fabrication processes that require a flat wafer surface can be performed. The oxide feature density non-uniformity affects the CMP process quality. It is a major cause of the post-CMP oxide topography variation.
Non-uniformity of the oxide feature density can be decreased by adding dummy metal features to low metal density regions of a metal layer. For example, some foundries use simple rule-base techniques to fill dummy features in low density regions to reduce post-CMP topography variation. Adding dummy features also helps to improve the quality of other CMP processes, such as, copper CMP and STI CMP.
Unfortunately, these simple rule-based techniques have many shortcomings because they do not accurately model the complex CMP process. In particular, these techniques can sometimes be ineffective because they add dummy features at suboptimal locations. Moreover, these techniques can be inefficient because they add more dummy features than necessary.
Smart dummy filling techniques, on the other hand, use more accurate CMP models to determine the amount and location of dummy features.
Smart dummy filling techniques based on LP (Linear Programming) can produce optimal results in the case of oxide CMP. But, these techniques can be extremely time-consuming. The computational cost of LP-based methods can become prohibitively high for large layouts with multiple layers. Consequently, LP-based methods are not practical for many real-life dummy-filling scenarios.
On the other hand, greedy heuristics and Monte Carlo methods are computationally efficient, but generate sub-optimal dummy-fillings. Unfortunately, due to the continuing miniaturization of feature sizes, sub-optimal dummy-fillings are not sufficient to significantly improve yields and reduce performance variations.
Hence, what is needed is an efficient method for computing substantially-optimal dummy feature density for a CMP process to reduce the post-CMP topography variation.